data mining

Examples of data mining in the following topics:

• Data Snooping: Testing Hypotheses Once You've Seen the Data

  • Testing hypothesis once you've seen the data may result in inaccurate conclusions.
  • The error is particularly prevalent in data mining and machine learning.
  • Sometimes, people deliberately test hypotheses once they've seen the data.
  • Data snooping (also called data fishing or data dredging) is the inappropriate (sometimes deliberately so) use of data mining to uncover misleading relationships in data.
  • Although data-snooping bias can occur in any field that uses data mining, it is of particular concern in finance and medical research, which both heavily use data mining.

• Bioinformatic Analyses and Gene Distributions

  • Bioinformatics is the study of methods for storing, retrieving and analyzing biological data.
  • Bioinformatics also deals with algorithms, databases and information systems, web technologies, artificial intelligence and soft computing, information and computation theory, structural biology, software engineering, data mining, image processing, modeling and simulation, discrete mathematics, control and system theory, circuit theory, and statistics.
  • Examples include pattern recognition, data mining, machine learning algorithms, and visualization.
  • annotate genes and gene products and assimilate and disseminate annotation data
  • offer tools for easy access to all aspects of the data provided by the project

• The Value of Analytics in Decision Making

  • Predictive and descriptive analytics are two methods of using data and statistical methods to assess actual outcomes against target standards and goals.
  • Predictive analytics encompass a variety of statistical techniques (such as modeling, machine learning, and data mining) that analyze current and historical facts to make estimates about future events.
  • Data mining draws on large numbers of records to identify patterns that can then be identified as opportunities or risks.
  • This approach seeks to understand past performances by using historical data to analyze the reasons behind past success or failure.
  • These tools create tables, charts, and graphs to present the data visually, which can help to clearly communicate the meaning of the data.

• Analyzing Data

  • Data Analysis is an important step in the Marketing Research process where data is organized, reviewed, verified, and interpreted.
  • Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes.
  • In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA).
  • All are varieties of data analysis.
  • Summarize the characteristics of data preparation and methodology of data analysis

• Exploratory Data Analysis (EDA)

  • Exploratory data analysis is an approach to analyzing data sets in order to summarize their main characteristics, often with visual methods.
  • Exploratory data analysis (EDA) is an approach to analyzing data sets in order to summarize their main characteristics, often with visual methods.
  • Exploratory data analysis was promoted by John Tukey to encourage statisticians to explore the data and possibly formulate hypotheses that could lead to new data collection and experiments.
  • Tukey promoted the use of the five number summary of numerical data:
  • Many EDA techniques have been adopted into data mining and are being taught to young students as a way to introduce them to statistical thinking.

• Applications of Statistics

  • In calculating the arithmetic mean of a sample, for example, the algorithm works by summing all the data values observed in the sample and then dividing this sum by the number of data items.
  • Statistical methods can summarize or describe a collection of data.
  • These inferences may take the form of: answering yes/no questions about the data (hypothesis testing), estimating numerical characteristics of the data (estimation), describing associations within the data (correlation) and modeling relationships within the data (for example, using regression analysis).
  • It can include extrapolation and interpolation of time series or spatial data and can also include data mining.
  • This Boxplot represents Michelson and Morley's data on the speed of light.

• Fundamentals of Statistics

  • Data collected about this kind of "population" constitutes what is called a time series.
  • Data collected about this kind of "population" constitutes what is called a time series.
  • Numerical descriptors include mean and standard deviation for continuous data types (like heights or weights), while frequency and percentages are more useful in terms of describing categorical data (like race).
  • These inferences may take the form of: answering yes/no questions about the data (hypothesis testing), estimating numerical characteristics of the data (estimation), describing associations within the data (correlation ) and modeling relationships within the data (for example, using regression analysis).
  • It can include extrapolation and interpolation of time series or spatial data, and can also include data mining.

• Measuring the Market

  • The demand for cars in India creates demands for steel, tires, forgings, castings, and plastic components which in turn create demands for mining, rubber, forging machines, casting sand and polymers.
  • A customer value model (CVM) is a data-driven representation of the worth, in monetary terms, of what a company is doing or could do for its customers.
  • The amount and detail of customer data is now mined for its value to supply chain decisions and the bottom line.
  • The CVM uses data from customer interaction, on-site interviews, customer service data, sales force reports and all the other types of input and observations about product benefits and the bottom line.
  • Companies are looking beyond traditional assumptions and adopting new frameworks, theories, models and concepts based upon customer data and input.

• Productivity Gains from Technology

  • Mining and metal refining technologies played a key role in technological progress.
  • Railroads evolved from mine carts and the first steam engines were designed specifically for pumping water from mines.
  • Mining and metal refining technologies played a key role in technological progress.
  • Railroads evolved from mine carts and the first steam engines were designed specifically for pumping water from mines.
  • Railroads evolved from mine carts and the first steam engines were designed specifically for pumping water from mines.

• The Coal Strike of 1902

  • The Coal Strike of 1902 was a strike by the United Mine Workers of America in the anthracite coal fields of eastern Pennsylvania .
  • The strike never resumed, as the miners received more pay for fewer hours, however, the mine owners refused to recognize the trade union as a bargaining agent.
  • Wright used the staff of the Department of Labor to collect data about the cost of living in the coalfields.
  • By and large, social conditions in mine communities were found to be good, and miners were judged as only partly justified in their claim that annual earnings were not sufficient "to maintain an American standard of living. "
  • While the operators refused to recognize the United Mine Workers, they were required to agree to a six-man arbitration board, made up of equal numbers of labor and management representatives, with the power to settle labor disputes.